Abstract:Transductive support vector machine (TSVM) is a well-known algorithm that integrates transductive learning into support vector machine. In this paper, a bi-fuzzy progressive transductive support vector machine (BFPTSVM) is constructed by introducing the bi-fuzzy memberships and sample-pruning strategy for the temporary labeled samples. BFPTSVM is capable of degrading the computational complexity and the store memory of TSVM. Simulation results show that BFPTSVM has better classification and convergence performance compared with other learning algorithms.
[1] Vapnik V N. The Natural of Statistical Learning Theory. New York, USA: Springer-Verlag, 1995 [2] Joachims T. Transductive Inference for Text Classification Using Support Vector Machines // Proc of the 16th International Conference on Machine Learning. Bled, Slovenia, 1999: 200-209 [3] Chapelle O, Chi M, Zien A. A Continuation Method for Semi-Supervised SVMs // Proc of the 23rd International Conference on Machine Learning. Pittsburgh, USA, 2006: 185-192 [4] Astorino A, Fuduli A. Nonsmooth Optimization Techniques for Semi-Supervised Classification. IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29(12): 2135-2142 [5] Tian Yingjie, Yan Manfu. Unconstrained Transductive Support Vector Machines // Proc of the 4th International Conference on Fuzzy System Knowledge Discovery. Haikou, China, 2007, Ⅱ: 181-185 [6] Silva M M, Maia T T, Braga A P. An Evolutionary Approach to Transduction in Support Vector Machines // Proc of the 5th International Conference on Hybrid Intelligence System. Kitakyushu, Japan, 2005: 329-334 [7] Sun Fun, Sun Maosong. A New Transductive Support Vector Machine Approach to Text Categorization // Proc of the IEEE International Conference on Natural Language Processing and Knowledge Engineering. Beijing, China, 2005: 631-635 [8] Chen Yisong, Wang Guoping, Dong Shihai. Learning with Progressive Transductive Support Vector Machine. Pattern Recognition Letters, 2003, 24(6): 1845-1855 [9] Bruzzone L, Chi M, Marconcini M. A Novel Transductive SVM for Semisupervised Classification of Remote-Sensing Images. IEEE Trans on Geoscience and Remote Sensing, 2006, 44(11): 3363-3373 [10] Wang Lei, Jia Huading, Sun Shixin. A Fast and Accurate Progressive Algorithm for Training Transductive SVMs // Proc of the 4th International Symposium on Neural Networks: Advances in Neural Networks. Nanjing, China, 2007, Ⅲ: 497-505 [11] Wang Lei. Research on the Learning Algorithm of Support Vector Machines. Ph.D Dissertation. Chengdu, China: University of Electronic Science and Technology of China. School of Computer Science and Engineering, 2007: 90-106 (in Chinese) (王 磊.支持向量机学习算法的若干问题研究.博士学位论文.成都:电子科技大学.计算机科学与工程学院, 2007: 90-106) [12] Liu H, Huang S T. Fuzzy Transductive Support Vector Machines for Hypertext Classification. International Journal of Uncertainty, Fuzziness Knowledge-Based Systems, 2004, 12(1): 21-36